Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.

中文翻译：

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分子对接中用于蛋白质-配体相互作用的评分功能概述。

当前，分子对接正成为药物发现和分子建模应用中的关键工具。分子对接的可靠性取决于采用的评分功能的准确性，当生成数千种可能的配体位姿时，该功能可以指导和确定配体位姿。评分功能可用于确定配体的结合模式和位点，预测结合亲和力并确定给定蛋白质靶标的潜在药物前导。尽管多年来进行了深入研究，但是准确快速地预测蛋白质-配体相互作用仍然是分子对接中的一个挑战。因此，本研究基于最新的分类方案，回顾了四种评分函数的基本类型，即基于物理学的，基于经验的，基于知识的以及基于机器学习的评分函数。